CN101413806B - Mobile robot grating map creating method of real-time data fusion - Google Patents

Abstract

The invention provides a building method of a grid map by a mobile robot based on real-time data fusion. The method comprises the following steps: artificially dividing an environment into a plurality of grids with the same size, and obtaining the distance information by sonar sensors arranged on the front end of the mobile robot; extracting measured values of three sonar sensors which are closest to a currently computed grid unit at the same time, respectively explaining a single sonar data by a fuzzy logic and a probability theory so as to obtain a group of characteristic vectors which are subject to the data fusion as input vectors of a neural network, wherein, output values of the neural network comprises idle state, busy state and undefined state of the grid; finally updating the state of the grid by the Bayes rule. The mobile robot of the invention obtains the environmental information by sonar ranging finders and completes the environmental modeling, thus providing reliable basis for the subsequent autonomous navigation of the mobile robot.

Description

The mobile robot grating map creating method that a kind of real time data merges

Technical field

The invention belongs to robot and field of artificial intelligence, relate to the mobile robot grating map creating method that a kind of real time data merges.

Background technology

Over nearly 20 years, artificial intelligence technology and fast development of computer technology, autonomous intelligence mobile robot's research has obtained great concern.Its requirement can be obtained ambient condition information automatically by self contained sensor such as stadimeter, video camera, infrared etc., sets up space environment model and identification self current location, moves along the active path of planning automatically, thereby finishes particular task.Intelligent mobile robot is widely used in industries such as industrial or agricultural, communications and transportation, military affairs, health care at present, solves work problem and the human hard work of replacement under the hazardous environment.For improving ability to work and the range of application of mobile robot under circumstances not known, for being fit to the map of robot " understanding ", promptly map building is a crucial difficult problem of being badly in need of solution environment representation.

Mobile robot's working environment generally can be reduced to two dimensional model, and is described with taking grid.This model is divided into the several rules grid with robot place space environment, determines environment representation by the state (barrier, free time, the unknown) that extracts each grid cell.This model representation is directly perceived, is easy to create and safeguard.In circumstances not known, any prior imformation is not understood by robot, for example size of environment scale and barrier, shape, position etc., and do not exist in the environment such as the artificial object of reference of setting such as road sign, beacon.Map is created by robot can only depend on the information that its sensor obtains, as mileage gauge, sonar, laser or the like.Setting up the process of map, in fact is exactly robot according to the perception of sensor independently to the process of its environment of an activation modeling.Because the restriction of sensor self, there is uncertainty in various degree in perception information, need handle perception information usually again.Information processing in the map building can reduce following three problems:

(1) how the uncertainty of perception information is described;

(2) how to create map, not only will reflect perception information in the map, also will reflect the uncertainty of information according to uncertainty description to information;

(3) when same objective has been had new perception information, how to handle old information and fresh information, promptly upgrade map.

In Mobile Robotics Navigation, sonar sensor is owing to its cheapness, be simple and easy to characteristics such as usefulness, convenient data processing, therefore obtain widespread use in the mobile robot field, but also there are defectives such as multipath reflection, direct reflection, angular accuracy are low in sonar sensor itself, and there is bigger uncertainty in its perception information.In the research of map building, fuzzy logic and probability theory are two kinds of representative methods that are used to describe and handle uncertain information at present.The former is uncertain by sky, non-dummy section and state in the ambiguity in definition set representations environment, and each grid cell is calculated corresponding degree of membership according to measurement result; The latter provides the probability that it occupies for barrier to each grid cell, carries out information fusion according to bayes rule.Analysis shows that with experiment higher based on the accuracy of map that probability method produces, profile is clear, but too responsive to error message, the False Rate height; Fuzzy logic method then has higher robustness, and is still stable when the uncertain degree of information is high, but precision is low, and the map of generation is unintelligible.

Summary of the invention

Technical matters to be solved of the present invention provides the mobile robot grating map creating method that a kind of real time data merges, mobile robot of the present invention utilizes the sonar ranging instrument to obtain environmental information, finish environmental modeling, thereby provide reliable foundation for the follow-up independent navigation of mobile robot.

For solving the problems of the technologies described above, the technical solution adopted in the present invention is:

The mobile robot grating map creating method that a kind of real time data merges is characterized in that, may further comprise the steps:

1) with local grid map coordinate (i, j) initialization, and barrier that obtains by a plurality of sensors on the mobile robot and the distance value between the mobile robot;

2) 3 distance values with the numerical value minimum in the described distance value are arranged as one-dimension array with fuzzy logic and probability theory explanation; { O, E, U} represent the taking of grid in the map, free time and unknown state promptly each distance value to be set up 3 fuzzy sets; Ambiguity in definition vector T={ μ _O, μ _E, μ _URepresent that each grid is in the confidence level of three state, grid belongs to the degree of membership of three state and is 1;

μ _o(r, s), μ _E(r is that (i j) takies membership function and idle membership function apart from sensor distance r at the grid of zones of different to grid g s); μ _{O, E}(α, the β) membership function of the different beam axis line angle of expression, μ _{O, E}(r) membership function of expression different measuring distance;

The occupied membership function μ of each grid cell _o(r, α), the idle membership function μ of grid _E(r, α) and the uncertain membership function μ of trellis states could _U(r, α) by following calculating gained:

${\mathrm{\μ}}_E(r,s)=\left\{\begin{array}{cc}1& 0\≤r\≤s-\mathrm{\Δs}\\ \left|\frac{s-r}{\mathrm{\Δs}}\right|,& s-\mathrm{\Δs}\≤r\≤s\\ 0& r\≥s\end{array}\right.;$

${\mathrm{\μ}}_{o,E}(\mathrm{\α},\mathrm{\β})=\left\{\begin{array}{cc}1-\left|\frac{\mathrm{\α}-\mathrm{\β}}{\mathrm{\β}}\right|,& \left|\mathrm{\α}\right|\≤\mathrm{\β}\\ 0& \left|\mathrm{\α}\right|>\mathrm{\β}\end{array}\right.;$

${\mathrm{\&mu;}}_{O,E}\left(r\right)=\left\{\begin{array}{cc}1& r\&le;\frac{\mathrm{<figure-callout\; id="3"\; label="R"\; filenames="H2008101435378E00012.png"\; state="\{\{state\}\}">R</figure-callout>}}{3}\\ \frac{3(R-r)}{2R},& \frac{\mathrm{<figure-callout\; id="3"\; label="R"\; filenames="H2008101435378E00012.png"\; state="\{\{state\}\}">R</figure-callout>}}{3}<r\&le;R\end{array}\right.;$

μ _o(r，α)＝M ₀[μ _o(r，s)+μ _o，E(α，β)+μ _o，E(r)]/3；

μ _E(r，α)＝[μ _E(r，s)+μ _O，E(α，β)+μ _O，E(r)]/3；

μ _U(r，α)＝1-μ _O(r，α)-μ _E(r，α)；

Wherein, M ₀The occupied maximum likelihood of expression grid cell, value is less than 1; Wherein s is the sensor distance measured value, and Δ s is the estimation of error scope to obstacle distance S, and α is the angle of r with respect to the beam axis line; β represents the tapered width half-angle of wave beam; R represents the maximum detectable range of sensor;

Grid g (i, j) be occupied or idle probability is used P (taking) and P (free time) expression respectively, computing method are as follows:

P (taking)=1-P (free time);

Corresponding 5 the element { μ of each distance value _o(r, α), μ _E(r, α), μ _U(r, α), P (taking); P (free time) }, for 3 distance values, then described one-dimension array has 15 elements.

3) with described one-dimension array through Processing with Neural Network, be output as conditional probability vector O=[O _Occ, O _Emp, O _Uce], represent respectively grid cell g (x, y) corresponding three kinds may states the probable value of (barrier, dummy section, uncertain).

4) according to coordinate transform, with grid cell g (i, j) from local coordinate system project to the global coordinate system correspondence grid cell g (x, y); Utilize the Bayesian probability method to realize that respectively the probability of three kinds of possibility states upgrades then, according to the maximal value rule, the maximal value of getting the possibility state is the degree of confidence of current grid.

5) coordinate is updated to next grid cell, if trellis states could has not been upgraded, returns step 1); If trellis states could has been upgraded, then grating map creating is finished.

Described neural network interpretation model comprises three layers altogether: input layer, hidden layer and output layer; Wherein the input vector of input layer is described one-dimension array; The transport function of described hidden layer neuron adopts S type tan; The transport function that described output layer adopts is a S type logarithmic function, and node is output as the probable value of [0,1] scope.

Described Bayesian probability method is:

For grid cell m _{I, j}Three kinds may states initial probability be 1/3, then pass through measurement data sequence S=(s ⁽¹⁾..., s ^(T)) after, s wherein ⁽ⁿ⁾Represent three and the maximally related sensor measurement value sequence of current computation grid, corresponding integrated probability is as follows respectively:

In the formula, P (takies _t), the P (free time _t), P is (uncertain _t) be the final trellis states could value behind consideration historical information and the current information, O _Occ, O _Emp, O _UceBe current neural Network Data Fusion output, P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) be last one constantly final trellis states could value, i.e. prior probability, when t=1, P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) value be the initial probability 1/3 of agreement;

At last P (is taken _t), the P (free time _t), P is (uncertain _t) get maximal value, be the degree of confidence of current grid.

Described sensor is 8.

The position with respect to the robot forward direction of 8 sensors is: ± 10 °, ± 30 °, ± 50 ° and ± 90 °.

As improvement, get M ₀=0.95.

The beneficial effect that the present invention had:

The present invention proposes the mobile robot grating map creating method that a kind of real time data merges, and is used to describe the sky and the non-dummy section of mobile robot's environment of living in.At first artificially environment is divided into the grid of several sizes such as grade, obtains range information by the sonar sensor that is installed in mobile robot's front end; Extract and the measured value of nearest three sonar sensors of current computation grid cell distance at synchronization, adopt fuzzy logic and probability theory to explain single sonar data respectively, promptly represent the taking of certain grid, empty and uncertain with fuzzy logic, represent to occur in certain grid probability and the empty probability that barrier occupies with probability theory according to range information.Can obtain an eigenvectors thus, and carry out data fusion as the input vector of neural network, the output valve of neural network is grid free time, occupied or nondeterministic statement.Adopt Bayes rule to upgrade the state of grid at last.Mobile robot of the present invention utilizes the sonar ranging instrument to obtain environmental information, finishes environmental modeling, thereby provides reliable foundation for the follow-up independent navigation of mobile robot.

Compared with prior art, advantage of the present invention just is:

1. because the training of neural network based on sample, has adaptivity soon to new environment;

2. fuzzy logic, probability theory are explained that respectively the result of sonar data merges, effectively combine fuzzy logic interpretation model robustness height, advantage that probability theory interpretation model precision is high;

3. can handle a plurality of sensor informations simultaneously, consider the influence of a plurality of sensors to the measurement result of same grid, associating ambient sensors information can generate more accurate result, can solve direct reflection and other uncertain problem well.

Description of drawings

Fig. 1 grating map creating method schematic diagram of the present invention;

The configuration of Fig. 2 mobile robot forward direction sonar ring;

Fig. 3 sonar sensor model;

Fig. 4 neural Network Data Fusion is created grating map;

Fig. 5 training sample environment;

Fig. 6 takies grid and obtains the sample sample.

Embodiment

The invention will be further described below in conjunction with accompanying drawing.

Embodiment 1:

The present invention proposes the mobile robot grating map creating method that a kind of real time data merges, and its systematic schematic diagram as shown in Figure 1.The sonar data uncertainty that adopts neural network that probability theory, fuzzy logic are explained is carried out information fusion, and has considered the influence of the spatial coherence of a plurality of sonars to same trellis states could, to set up the environment grating map.

S0, S1 among Fig. 1 ..., S7 is the sonar sensor distance measurement value that is installed in mobile robot's front end, O _Emp{ x, y}, O _Occ{ x, y}, O _Uce{ x, y} are respectively the idle probability of grid, barrier probability and the nondeterministic statement probability of current calculating.

The mobile robot adopts the ultrasonic ranging sensor to finish environmental modeling, and its front end is equipped with eight sonar ranging sensors.The position of mobile robot's sonar sensor is among Fig. 2: ± 10 °, ± 30 °, ± 50 ° and ± 90 °, be used to survey the information of barrier on the direction separately.

When the mobile robot explores in the ergodic process at environment, once gather sonar sensor measurement data sequence and corresponding robot global positional information.Be that local reference frame is set up at the center with the machine people then, create local map based on the sonar sensor measurement data.In local map building, a certain particular grid unit cell (i to local map, j), three at first relevant continuous sonar sensors according to the spatial positional information choice direction of corresponding this geometric units correspondence, use fuzzy logic (fuzzy set { take, sky, uncertain } is described) and probability theory (probability { barrier respectively with three sonar sensor ranging information, empty } describe) explain and can obtain 15 decryptions, with this input as neural network.Through (as shown in Figure 4) after the interpretation process of neural network, its corresponding neural network is output as conditional probability vector O=[O _Occ, O _Emp, O _Uce], represent respectively grid cell g (x, y) corresponding three kinds may states the probable value of (barrier, dummy section, uncertain).On this basis, according to coordinate transform, with grid cell g (i, j) from local coordinate system project to global coordinate system to deserved grid cell g (x, y).Utilize the Bayesian probability model to realize that respectively the probability of three kinds of possibility states upgrades then.

Sonar sensor model, fuzzy logic explain that sonar uncertainty models, probability theory explanation sonar uncertainty models, neural network fusion establishment grating map algorithm and Bayes upgrade grid and set forth respectively in 1,2,3,4,5 trifles.

1, sonar sensor model

Illustrate: single Shu Shengna basic model visual field determines that by β and R β represents the tapered width half-angle, and R represents maximum detectable range.The visual field can project on the regular net, is free time or occupied information because each unit of grid all records corresponding locus, takies grid so be called, and also claims to take grid.As shown in Figure 3, the visual field can be divided into three zones.

Area I: coherent element may be occupied;

Area I I: coherent element may be empty;

Area I II: coherent element situation the unknown;

For given range reading, " free time " of area I I has bigger possibility than area I " occupied ".No matter be " free time " or " occupied ", than more accurate towards the data of two edge directions, partly cause is that a limit along barrier may produce direct reflection or cause other apart from the perception mistake acoustic beam along the data of sound wave axis direction.

Though the sensor model among Fig. 3 is a kind of general choice, but how is model conversion to be very different aspect the confidence data, introduce fuzzy logic, the probability theory transfer process of being correlated with below respectively, be convenient to follow-up neural network and carry out data fusion.

2, fuzzy logic is explained the sonar uncertainty models

Illustrate: fuzzy logic explains that the basic thought of sonar uncertainty models is to set up 3 fuzzy sets { O, E, U} represent the taking of all grids in the map, free time and unknown state.Ambiguity in definition vector T={ μ _O, μ _E, μ _URepresent that each grid is in the reliability of three state, grid belongs to the degree of membership of three state and is 1.

Algorithm:

In Fig. 3, establishing the sonar range measured value is s, and Δ s is the estimation of error scope to obstacle distance s.(i j) represents with r to the distance of sensor, is α with respect to the angle of beam axis line for the arbitrary grid cell g in the beam coverage.In following formula (1)～(4), μ _o(r, s), μ _E(r is s) for to take membership function and idle membership function apart from sensor distance r at the grid of zones of different.μ _{O, E}(α, the β) membership function of the different beam axis line angle of expression, μ _{O, E}(r) membership function of expression different measuring distance.

The occupied membership function μ of each grid cell _o(r, α), the idle membership function μ of grid _E(r, α) and the uncertain membership function μ of trellis states could _U(r α) can calculate gained by formula (5)～(7).

${\mathrm{\&mu;}}_o(r,s)=\left\{\begin{array}{cc}1-\left|\frac{s-r}{\mathrm{\&Delta;s}}\right|,& s-\mathrm{\&Delta;s}\&le;r\&le;s+\mathrm{\&Delta;s}\\ 0& \mathrm{others}\end{array}\right.---\left(1\right)$

${\mathrm{\&mu;}}_E(r,s)=\left\{\begin{array}{cc}1& 0\&le;r\&le;s-\mathrm{\&Delta;s}\\ \left|\frac{s-r}{\mathrm{\&Delta;s}}\right|,& s-\mathrm{\&Delta;s}\&le;r\&le;s\\ 0& r\&GreaterEqual;s\end{array}\right.---\left(2\right)$

${\mathrm{\&mu;}}_{o,E}(\mathrm{\&alpha;},\mathrm{\&beta;})=\left\{\begin{array}{cc}1-\left|\frac{\mathrm{\&alpha;}-\mathrm{\&beta;}}{\mathrm{\&beta;}}\right|,& \left|\mathrm{\&alpha;}\right|\&le;\mathrm{\&beta;}\\ 0& \left|\mathrm{\&alpha;}\right|>\mathrm{\&beta;}\end{array}\right.---\left(3\right)$

${\mathrm{\&mu;}}_{O,E}\left(r\right)=\left\{\begin{array}{cc}1& r\&le;\frac{\mathrm{<figure-callout\; id="3"\; label="R"\; filenames="H2008101435378E00012.png"\; state="\{\{state\}\}">R</figure-callout>}}{3}\\ \frac{3(R-r)}{2R},& \frac{\mathrm{<figure-callout\; id="3"\; label="R"\; filenames="H2008101435378E00012.png"\; state="\{\{state\}\}">R</figure-callout>}}{3}<r\&le;R\end{array}\right.---\left(4\right)$

μ _o(r，α)＝M ₀[μ _o(r，s)+μ _o，E(α，β)+μ _o，E(r)]/3 (5)

μ _E(r，α)＝[μ _E(r，s)+μ _o，E(α，β)+μ _O，E(r)]/3 (6)

μ _U(r，α)＝1-μ _O(r，α)-μ _E(r，α) (7)

M in the formula (5) ₀The occupied maximum likelihood of expression grid cell because the occupied possibility of grid can not be 100%, is therefore got M ₀=0.95.

3, probability theory is explained the sonar uncertainty models

Illustrate: represent the occupied or idle condition of grid with probability function.

Algorithm:

Sonar can only be observed an incident: (i is occupied or idle j) to element g.This can be written as H={ and takies, the free time }.The true probability that takes place of H incident is represented with P (H):

0≤P(H)≤1

A critical nature of probability is: if know P (H), so H do not have probability of happening P (just known also that H) this can be expressed as:

1-P(H)＝P(-H)

(H) probability of form is called unconditional probability, a prior imformation only is provided, and does not consider sensor reading s for P (H) and P.Concerning robot, can according to sensor reading zoning g (i, j) function idle or occupied probability is more useful, this probability is conditional probability.When P (H|s) is exactly the concrete reading s of given sensor, the probability of the actual generation of H incident.Conditional probability also has such character: P (H|s)+P (H|s)=1.

For each grid cell in Fig. 3 area I:

P (free time)=1-P (taking) (9)

The implication of r, α and fuzzy logic are explained consistent in the sonar uncertainty models, M ₁Represent that occupied unit reading makes never that to take degree of confidence be 100%, gets M ₁=0.98.

For each grid cell among Fig. 3 area I I:

P (taking)=1-P (free time) (10)

Different with the area I grid cell, the idle probability of area I I element can reach 1.

4, neural Network Data Fusion is created grating map

Illustrate: in grating map creating, the measurement data of sonar sensor must be explained and be mapped as relevant position unit g (x, degree of confidence y).Yet there are problems such as multipath reflection, direct reflection, angular accuracy be low in sonar sensor, is difficult to set up precise math model and is used to explain sonar data.Because the multilayer neural network after the training can approach any probability distribution, so can utilize the neural network after the training to realize of the mapping of sonar to measure data to the grid probability.

Algorithm:

1) neural network structure

As shown in Figure 4, the neural network interpretation model that proposes of the present invention comprises three layers altogether: input layer, hidden layer and output layer.Below go through the design and the realization of each layer.

(1) input layer

The input vector of neural network comprises 15 elements.For given grid cell g (i, j), select with robot center and grid cell centerline direction maximally related about totally three 3 sonar to measure data that the sonar sensor synchronization gets access to.Then, receiving the fuzzy logic interpretation model of sensor according to monophone calculates each sonar to measure data (totally three groups of fuzzy sets is represented the state of grid, i.e. T for i, explanation j) to grid cell g ₁={ μ _O1, μ _E1, μ _U1,

T ₂={ μ _O2, μ _E2, μ _U3, T ₃={ μ _O3, μ _E3, μ _U3; Receiving the probability theory interpretation model of sensor according to monophone calculates each sonar to measure data (totally three groups of probable values is represented the state of grid, i.e. P for i, state j) to grid cell g ₁={ P _O1, P _E1, P ₂={ P _O2, P _E2, P ₃={ P _O3, P _E3.With these six groups of data totally 15 elements as the input of neural network, as follows:

ρ＝{μ _O1，μ _E1，μ _U1，μ _O2，μ _E2，μ _U2，μ _O3，μ _E3，μ _U3，P _O1，P _E1，P _O2，P _E2P _O3P _E3} ^T

(2) hidden layer

The neuron number of hidden layer is designed to 31, and the neuron number of hidden layer is not what fix, adjusts as required in hands-on.The transport function of hidden layer neuron adopts S type tan.

(3) output layer

Output layer has 3 nodes, and output valve is respectively O=[O _Occ, O _Emp, O _Uce], three kinds of expression grid cell correspondence may state.Wherein first output node represents that grid cell is that the probable value of seizure condition is O _Occ, second output node represents that grid cell is that the probable value of idle condition is O _Emp, the 3rd output node represents that grid cell is that the probable value of nondeterministic statement is O _UceThe transport function that this network layer adopted is a S type logarithmic function, and node output still is the probable value of [0,1] scope.

2) neural metwork training

Neural metwork training is the very important step of neural network, in case network training is finished, robustness and adaptivity owing to neural network self promptly can be used in the multiple different environment.Below introduce in detail and explain related training sample and the training algorithm of network about sonar.

(1) training sample

According to neural network model shown in Figure 4, the form of training data sample is as follows:

<μ _O1，μ _E1，μ _U1，μ _O2，μ _E2，μ _U2，μ _O3，μ _E3，μ _U3，P _O1，P _E1，P _O2，P _E2，P _O3，P _E3，O _occ，O _emp，O _uce>

Wherein＜μ _O1, μ _E1, μ _U1,＜μ _O2, μ _E2, μ _U2,＜μ _O3, μ _E3, μ _U3Represent respectively robot center and grid cell centerline direction maximally related about totally three 3 sonar to measure data that the sonar sensor synchronization obtains, receive the fuzzy logic interpretation model of sensor according to monophone and calculate each sonar to measure data grid cell g (i, explanation j);＜P _O1, P _E1,＜P _O2, P _E2,＜P _O3, P _E3Explain the explanation of 3 sonar to measure data that above-mentioned three sensor synchronizations got access to probability theory respectively; Grid cell g (i, desired output usefulness＜O j) _Occ, O _Emp, O _UceExpression, correspond respectively to take, the output of idle and nondeterministic statement, its possible output valve be [1,0,0], [0,1,0], [0,0,1].

The gatherer process of training sample is as follows.Robot is put into (being robot location, peripheral obstacle location aware) under the known indoor environment, and by the robot rectilinear motion, rotatablely move, continuous several times is gathered robot pose and sonar sensor measurement data at random, and＜O _Occ, O _Emp, O _UceCalculate gained cell (i, the corresponding output of real space state j) for considering grid cell and barrier spatial information.

Fig. 5 is the hands-on environment representation.The sonar ranging scope that adopts is R=300cm, field angle β=15 °, and tolerance is 15cm, grid size is decided to be 10cm * 10cm.

Below by obtaining of example explanation sample.

Take the zone that grid has covered one 30 * 24 unit as shown in Figure 6, also just can be expressed as one 30 * 24 two-dimensional array, consider particular grid g[7] [11] (among figure shown in the bullet), the robot location is at g[27] [11] (among figure shown in the black point), i.e. r=200cm.Synchronization obtains the measurement data of 8 sonars, with particular grid g[7] the maximally related sonar data in [11] position for be positioned among Fig. 2 ± 10 °, 30 ° sonar data (also can replace the position at 30 ° sonar fetched data-30 ° sonar data with the position because this moment grid and this two sensors equidistant).

● for the position is 10 ° sonar, and resultant range information is s=210cm, α=-10 °, wherein | α |≤| β |, r≤s ± tolerance, grid cell is in the zone of sonar model angle, in the range reading upper range, belong in the measured value coverage simultaneously.

Applying mechanically formula (1)～(7), can to obtain this trellis states could that this sonar data is represented with fuzzy logic be T ₁=0.367,0.475,0.158}; Because be in area I, applying mechanically this trellis states could of representing with probability theory formula (8)～(9) is P ₁=0.327,0.673}.

● for the position is-10 ° sonar, and resultant range information is 205cm, α=10 °, wherein | α |≤| β |, r≤s ± tolerance, grid cell in the range reading upper range, belong in the measured value coverage in the zone of sonar model angle simultaneously.Applying mechanically formula (1)～(7), can to obtain this trellis states could that this sonar data is represented with fuzzy logic be T ₂=0.475,0.367,0.158}; Because be in area I, applying mechanically this trellis states could of representing with probability theory formula (8)～(9) is P ₂=0.327,0.673}.

● for the position is 30 ° sonar, and resultant range information is 110cm, α=-30 °.Wherein | α | | β |, r＜s+ tolerance, though in the range reading upper range, grid cell is outside the zone of sonar model angle, so do not belong in the measured value coverage, the fuzzy logic of this trellis state represent with to(for) this sonar data is T ₃=0,0, and 1}, this trellis states could of representing with probability theory is P ₃=0,1}.

● this grid is for taking, so O=[1 is arranged, 0,0 in the reality].

Obtain a sample data like this, by T ₁, T ₂, T ₃, P ₁, P ₂, P ₃, O forms, and is as follows:

＜0.367,0.475,0.158,0.475,0.367,0.158.0,0,1,0.327,0.673,0.327,0.673,0,1,1,0,0〉all the other samples can produce by same procedure.

(2) neural network BP training algorithm

In this paper neural metwork training, adopt classical Levenberg-Marquardt algorithm that neural network is trained, learning rate is 0.01, selects for use general approximate mean square deviation function as performance index function, when error is 2.5 * 10 ^-5Shi Xunlian finishes.

5, Bayes upgrades grid

After neural metwork training finished, the mobile robot walked traversal along the barrier edge lines in space environment, obtain space environment information, and utilize neural network that the sensor array of being gathered is made an explanation.Therefore to same grid cell, may there be different a plurality of explanations constantly.For obtaining to explain more accurately, need carry out integrated to these data.For avoiding complexity of calculation, guarantee that the enhancement formula of map building algorithm is handled, integration mode still adopts Bayesian (Bayes) integrated model.

Because this neural network model is exported the probability about three kinds of states of grid cell simultaneously, therefore in the map building process, carry out integrated to three kinds of state probability historical datas of grid cell respectively.For grid cell m _{I, j}Three kinds may states initial probability be 1/3, then pass through measurement data sequence S=(s ⁽¹⁾..., s ^(T))Back (s wherein ⁽ⁿ⁾Represent three and the maximally related sensor measurement value sequence of current computation grid), corresponding integrated probability is as follows respectively:

In the formula, P (takies _t), the P (free time _t), P is (uncertain _t) consider the final trellis states could value behind historical information and the current information, O _Occ, O _Emp, O _UceBe current neural Network Data Fusion output, P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) be last one constantly final trellis states could value, promptly prior probability (calculates) when t=1 for the first time, and P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) value be the initial probability 1/3 of agreement.

At last P (is taken _t), the P (free time _t), P is (uncertain _t) get maximal value, be the degree of confidence of current grid.

Claims (5)

1. the mobile robot grating map creating method that real time data merges is characterized in that, may further comprise the steps:

1) with local grid map coordinate (i, j) initialization, and barrier that obtains by a plurality of sensors on the mobile robot and the distance value between the mobile robot;

2) 3 distance values with the numerical value minimum in the described distance value are arranged as one-dimension array with fuzzy logic and probability theory explanation; { O, E, U} represent the taking of grid in the map, free time and unknown state promptly each distance value to be set up 3 fuzzy sets; Ambiguity in definition vector T={ μ _O, μ _E, μ _URepresent that each grid is in the confidence level of three state, grid belongs to the degree of membership of three state and is 1;

μ _o(r, s), μ _E(r is that (i j) takies membership function and idle membership function apart from sensor distance r at the grid of zones of different to grid g s); μ _{O, E}(α, the β) membership function of the different beam axis line angle of expression, μ _{O, E}(r) membership function of expression different measuring distance;

The occupied membership function μ of each grid cell _o(r, α), the idle membership function μ of grid _E(r, α) and the uncertain membership function μ of trellis states could _U(r, α) by following calculating gained:

${\mathrm{\&mu;}}_E(r,s)=\left\{\begin{array}{c}1\\ \left|\frac{s-r}{\mathrm{\&Delta;s}}\right|\\ 0\end{array}\right.,\begin{array}{c}0\&le;r\&le;s-\mathrm{\&Delta;s}\\ s-\mathrm{\&Delta;s}\&le;r\&le;s\\ r\&GreaterEqual;s\end{array}$

${\mathrm{\&mu;}}_{o,E}(\mathrm{\&alpha;},\mathrm{\&beta;})=\left\{\begin{array}{c}1-\left|\frac{\mathrm{\&alpha;}-\mathrm{\&beta;}}{\mathrm{\&beta;}}\right|\\ 0\end{array}\right.,\begin{array}{c}\left|\mathrm{\&alpha;}\right|\&le;\mathrm{\&beta;}\\ \left|\mathrm{\&alpha;}\right|>\mathrm{\&beta;}\end{array};$

${\mathrm{\&mu;}}_{O,E}\left(r\right)=\left\{\begin{array}{c}1\\ \frac{3(R-r)}{2R}\end{array}\right.,\begin{array}{c}r\&le;\frac{R}{3}\\ \frac{R}{3}<r\&le;R\end{array}$

μ ₀(r，α)＝M ₀[μ ₀(r，s)+μ _0，E(α，β)+μ _0，E(r)]/3；

μ _E(r，α)＝[μ _E(r，s)+μ _O，E(α，β)+μ _O，E(r)]/3；

μ _U(r，α)＝1-μ _O(r，α)-μ _E(r，α)；

Wherein, M ₀The occupied maximum likelihood of expression grid cell, value is less than 1; Wherein s is the sensor distance measured value, and Δ s is the estimation of error scope to obstacle distance S, and α is the angle of r with respect to the beam axis line; β represents the tapered width half-angle of wave beam; R represents the maximum detectable range of sensor;

Grid g (i, j) be occupied or idle probability is used P (taking) and P (free time) expression respectively, computing method are as follows:

P (taking)=1-P (free time);

Corresponding 5 the element { μ of each distance value _o(r, α), μ _E(r, α), μ _U(r, α), P (taking); P (free time) }, for 3 distance values, then described one-dimension array has 15 elements;

3) with described one-dimension array through Processing with Neural Network, be output as conditional probability vector O=[O _Occ, O _Emp, O _Uce], represent grid cell g (i, j) probable value of corresponding barrier, dummy section, uncertain these three kinds possibility states respectively;

4) according to coordinate transform, with grid cell g (i, j) from local coordinate system project to the global coordinate system correspondence grid cell g (x, y); Utilize the Bayesian probability method to realize that respectively the probability of three kinds of possibility states upgrades then, according to the maximal value rule, the maximal value of getting the possibility state is the degree of confidence of current grid;

5) coordinate is updated to next grid cell, if trellis states could has not been upgraded, returns step 1); If trellis states could has been upgraded, then grating map creating is finished;

Described neural network comprises three layers altogether: input layer, hidden layer and output layer; Wherein the input vector of input layer is an one-dimension array; The transport function of hidden layer neuron adopts S type tan; The transport function that output layer adopts is a S type logarithmic function, and node is output as the probable value of [0,1] scope.

2. the mobile robot grating map creating method that real time data according to claim 1 merges is characterized in that described Bayesian probability method is:

For grid cell m _{I, j}Three kinds may states initial probability be 1/3, then pass through measurement data sequence S=(s ⁽¹⁾..., s ^(T)) after, s wherein ⁽ⁿ⁾Represent three and the maximally related sensor measurement value sequence of current computation grid, corresponding integrated probability is as follows respectively:

In the formula, P (takies _t), the P (free time _t), P is (uncertain _t) be the final trellis states could value behind consideration historical information and the current information, O _Occ, O _Emp, O _UceBe current neural Network Data Fusion output, P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) be last one constantly final trellis states could value, i.e. prior probability, when t=1, P (takies _T-1), the P (free time _T-1), P is (uncertain _T-1) value be the initial probability 1/3 of agreement;

At last P (is taken _t), the P (free time _t), P is (uncertain _t) get maximal value, be the degree of confidence of current grid.

3. the mobile robot grating map creating method that real time data according to claim 1 merges is characterized in that described sensor is 8.

4. the mobile robot grating map creating method that real time data according to claim 3 merges is characterized in that the position with respect to the robot forward direction of described 8 sensors is: ± 10 °, ± 30 °, ± 50 ° and ± 90 °.

5. the mobile robot grating map creating method that merges according to each described real time data of claim 1 to 4 is characterized in that, gets M ₀=0.95.

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